Cognaptus Insights

HippoCamp shows why personal AI agents fail less at finding files than at proving they understand the life those files describe.

A mechanism-first reading of how activation-level monitoring can detect hidden coordination among AI agents before surface behavior reveals the strategy.

A mechanism-first reading of MONA’s Camera Dropbox extension, showing why learned approval can suppress reward hacking without recovering useful capability.

A decision-theoretic reading of why useful AI agents need to price information, latency, congestion, and uncertainty before they ask one more question.

A mechanism-first reading of why structured intent frameworks improve AI alignment, where the evidence is strongest, and where too much structure becomes its own tax.

A study of medical teams shows why physiological synchrony should be treated as a pivotal-moment signal, not a simple collaboration score.

A mechanism-first reading of uncertainty gating, showing when post-hoc AI explanations should be generated, escalated, or withheld before they become expensive nonsense.

A business-focused reading of ScoringBench, showing why model evaluation metrics are not bookkeeping details but risk-pricing decisions.

A mechanism-first reading of Entropic Claim Resolution, and why enterprise RAG should select evidence that resolves uncertainty rather than evidence that merely sounds relevant.

A mechanism-first reading of AIGENIE, the R package that turns LLM-generated survey items into structurally screened candidate scales before human pilot testing begins.